Modelling palaeoecological datasets using a state-space approach: two case-studies

Quinn Asena, Jack Williams, and Tony Ives

2025-08-09

People


  • Jack Williams
  • Tony Ives
  • Angie Perotti
  • Nora Schlenker
  • David Nelson
  • Bryan Schuman
  • Jonathan Johnson
  • Vania Stefanova

multinomialTS background


A state-space approach to modelling multinomially distributed community data.


  • ESA 2023: presented early development
  • ESA 2024: ran workshop on how to fit multinomialTS
  • ESA 2025!: presenting developed use-cases
  • Do you have multinomially distributed data?
  • I’ll try to leave a couple of extra minutes for discussion.

Beyond pattern recognition


The cutting edge in palaeoecology is to establish potential relationships between patterns observed in species relative abundances and environmental covariates. For example, are observed patterns driven by:

  • Species interactions?
  • Climate variability?
  • Fire regime?
  • …

This is what we want to know if we are to use palaeoecology to inform management of contemporary ecosystems or inform potential future ecosystem states. No easy task!

State-space modelling


State-space modelling goes beyond descriptive approaches and attempts to estimate:


  • Autoregressive / density dependent processes
  • Interspecific interactions
  • Species-environment interactions
  • Combinations of the above

multinomialTS

  • Models a multinomial distribution (i.e., count data) directly
  • Accepts multiple covariates of different type
  • Incorporates process error and observation error
  • Asena et al., in review

Case studies


Tulane

  • Florida
  • ~60,000 year-long record
  • Covariates (regional/global)
    • fungal spores (proxy for megaherbivory)
    • \(CO_2\) and \(\delta18O\) (climate)
    • Heinrich events (climate-related)
    • charcoal (fire)
  • Williams et al., in prep, Grimm et al. (1993); Grimm et al. (2006)

Sunfish

  • Pennsylvania
  • ~13,000 years
  • Covariates (local)
    • lake level (proxy for humidity)
  • Johnson et al., in review

Fitting process


  • Set up multiple working hypotheses
  • Choose focal taxa and reference group
  • Choose window-span/prediction resolution
  • Fit model to hypotheses or components of each hypothesis

Showing results for best model!

Tulane variables

Fitting Tulane


Questions/hypotheses:

  • Are biotic interactions or climatic variability the primary drivers of change?
  • Is mega-herbivory a driver of vegetation change?
  • is fire a driver of vegetation change?

Parameter selection

  • Window span / prediction resolution 200 years
  • Species/taxonomic group selection
    • two functional groups
    • two key dominant taxa
  • Interactions estimated:
    • pine - oak (in this example)

Tulane results

Supported hypotheses: given the data, species interactions are important but climate covariates have a stronger influence.

Sunfish description

Fitting Sunfish


Questions/hypotheses:

  • Are local climatic conditions (humidity) a stronger driver of change than species interactions?

Parameter selection

  • Window span / prediction resolution 100 years
  • Species/taxonomic group selection
    • five most dominant species
    • eastern white pine, hemlock, beech, oak, and birch
  • Interactions:
    • pine - hemlock (in this example)

Sunfish results

Supported Hypotheses: local conditions most important to Hemlock but interactions are important among other species. Pairwise interactions not shown here.

Comparison of case-studies


Results are not causal but lend support hypotheses.


Tulane

  • Long record
  • Multiple covariates
  • Fewer dominant taxa
  • Key drivers and interactions detected
  • Likely a loss of fine-scale processes like post-fire recovery

Sunfire

  • Holocene record
  • Single covariate
  • Multiple taxa
  • Finer-scale resolution
  • Likely to be missing explanitory power from unmeasured drivers

Thank you for listening!

References

Grimm, Eric C., George L. Jacobson, William A. Watts, Barbara C. S. Hansen, and Kirk A. Maasch. 1993. “A 50,000-Year Record of Climate Oscillations from Florida and Its Temporal Correlation with the Heinrich Events.” Science 261 (5118): 198–200. https://doi.org/10.1126/science.261.5118.198.
Grimm, Eric C., William A. Watts, George L. Jacobson, Barbara C. S. Hansen, Heather R. Almquist, and Ann C. Dieffenbacher-Krall. 2006. “Evidence for Warm Wet Heinrich Events in Florida.” Quaternary Science Reviews 25 (17): 2197–2211. https://doi.org/10.1016/j.quascirev.2006.04.008.